Authors:
Johannes Schneider
1
;
Bin Lu
2
;
Thomas Locher
1
;
Yvonne-Anne Pignolet
1
;
Matus Harvan
1
and
Sebastian Obermeier
1
Affiliations:
1
ABB Corporate Research, Switzerland
;
2
Ecole Polytechnique Federale de Lausanne, Switzerland
Keyword(s):
Secure Computing, Remote Data Processing, Privacy-preserving Cloud Computing, Security Engineering, Industrial Systems.
Related
Ontology
Subjects/Areas/Topics:
Data and Application Security and Privacy
;
Information and Systems Security
;
Privacy
;
Privacy Enhancing Technologies
;
Security and Privacy for Big Data
;
Security and Privacy in IT Outsourcing
;
Security and Privacy in the Cloud
;
Security Protocols
Abstract:
Homomorphic encryption and secure multi-party computation enable computations on encrypted data. However,
both techniques suffer from a large performance overhead. While advances in algorithms might reduce
the overhead, we show that achieving perfect (or even computational) confidentiality is not possible without
increasing the running time compared to computations on plaintext more than exponentially in some cases. In
practice, however, perfect confidentiality is not always required. The paper discusses mechanisms to trade off
confidentiality and performance for computing on ciphertexts. It introduces a fine-grained approach to define
security levels for variables called (statistical) subdomain privacy. This concept differs substantially from
prior work because it treats a variable as confidential or non-confidential depending on the actual value. We
further propose privacy-preserving methods for memory access patterns. We apply our techniques to improve
performance of control flow
logic (loops, if-then-else logic) and arithmetic operations such as multiplications.
The evaluation shows that the resulting speedup can be in the order of several magnitudes depending on the
privacy needs.
(More)